### 抄録

Bayesian Network is a stochastic model, which shows the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. This paper deals with the production and inventory control using the dynamic Bayesian network. The probabilistic values of the amount of delivered goods and the production quantities are changed in the real environment, and then the total stock is also changed randomly. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the Bayesian network. Moreover, an adjusting rule of the production quantities to maintain the probability of the lower bound and the upper bound of the total stock to certain values is shown.

元の言語 | English |
---|---|

ページ（範囲） | 148-154 |

ページ数 | 7 |

ジャーナル | Artificial Life and Robotics |

巻 | 13 |

発行部数 | 1 |

DOI | |

出版物ステータス | Published - 2008 |

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### ASJC Scopus subject areas

- Artificial Intelligence
- Biochemistry, Genetics and Molecular Biology(all)

### これを引用

*Artificial Life and Robotics*,

*13*(1), 148-154. https://doi.org/10.1007/s10015-008-0581-x

**Stochastic model of production and inventory control using dynamic bayesian network.** / Shin, Ji Sun; Lee, Tae Hong; Kim, Jin Il; Lee, HeeHyol.

研究成果: Article

*Artificial Life and Robotics*, 巻. 13, 番号 1, pp. 148-154. https://doi.org/10.1007/s10015-008-0581-x

}

TY - JOUR

T1 - Stochastic model of production and inventory control using dynamic bayesian network

AU - Shin, Ji Sun

AU - Lee, Tae Hong

AU - Kim, Jin Il

AU - Lee, HeeHyol

PY - 2008

Y1 - 2008

N2 - Bayesian Network is a stochastic model, which shows the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. This paper deals with the production and inventory control using the dynamic Bayesian network. The probabilistic values of the amount of delivered goods and the production quantities are changed in the real environment, and then the total stock is also changed randomly. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the Bayesian network. Moreover, an adjusting rule of the production quantities to maintain the probability of the lower bound and the upper bound of the total stock to certain values is shown.

AB - Bayesian Network is a stochastic model, which shows the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. This paper deals with the production and inventory control using the dynamic Bayesian network. The probabilistic values of the amount of delivered goods and the production quantities are changed in the real environment, and then the total stock is also changed randomly. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the Bayesian network. Moreover, an adjusting rule of the production quantities to maintain the probability of the lower bound and the upper bound of the total stock to certain values is shown.

KW - Dynamic Bayesian Network

KW - Graphical Modeling

KW - Probability distribution

KW - Production inventory control

UR - http://www.scopus.com/inward/record.url?scp=58049216458&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=58049216458&partnerID=8YFLogxK

U2 - 10.1007/s10015-008-0581-x

DO - 10.1007/s10015-008-0581-x

M3 - Article

AN - SCOPUS:58049216458

VL - 13

SP - 148

EP - 154

JO - Artificial Life and Robotics

JF - Artificial Life and Robotics

SN - 1433-5298

IS - 1

ER -